Because of the advantages in great capacity, high service quality and excellent secrecy property, CDMA mobile communication system has become tendency of 3G mobile communication developments. Wherein, multiple user detection (MUD) is one kind of enhanced technique that can overcome limitations on CDMA system capacity produced by multiple access interference (MAI), in order to improve capacity and performance of the CDMA system.
With help of the information of multi-users, MUD technique can jointly detect signals from multi-users, in order to reduce influences of MAI on performance of the receiver, and heighten capacity of the system as much as possible. At present, MUD comprises maximum likelihood sequence detector that is the best detector, linear multiple user detector and interference cancellation multiple user detector that are second best detectors. Wherein, interference cancellation multiple user detection comprises the step of regarding signal from expected user as useful signal, removing and interference of other users from received signals to obtain signal of expected user, and then detecting the signal of the said expected user. In this way, performance of the system can be largely raised.
The interference cancellation multiple user detection can be classified into a serial interference cancellation (SIC) and a parallel interference cancellation (PIC). SIC sorts the user signals in power descending order and cancels interference in serial, the method has a better performance compared with single user detection but with longer time delay, and must sort signals of the users according to their power, which needs heavy calculation and is sensitive to the original signals estimation. PIC removes interference of other users in parallel from received signals, which has the advantages of short time delay and simpler calculation.
Prior PIC methods comprise traditional PIC method, partial PIC method and weighting PIC method that is based on Bayes rule.
Compared with single-user detection, the traditional PIC method can raise system performance in a large extent under high signal-to-noise ratio, but has a lower raise under low signal-to-noise ratio.
Being different from the traditional PIC method which removes MAI influence on expected users completely from received signals, the partial PIC method sets a weight value for each stage of interference cancellation to weight the MAI influence on the expected users, and partially removes the MAI during the interfere cancellation process. The traditional PIC method within Gaussian channel removes all the MAI on the expected users from the received signals, of course, the signal estimation on the expected users under this circumstance is biased; on the other hand, partial PIC method merely removes the MAI partially, which can correct estimation biases on the expected users, making decision results more reliable. At circumstance of low signal-to-noise ratio, partial PIC method has obviously better performance than that of the traditional PIC method.
The patent U.S. Pat. No. 5,418,814 discloses the weighting PIC method that is based on Bayes rule. Although being a weighting method, its weighting principle, which is a symbol-level weighting method that is based on a minimum mean of decision cost, is different from that of the partial PIC method. When creating a decision cost function, the method takes the minimum mean of decision cost as rule, to determine reliability coefficient of the decision result of each symbol, and to make symbol level weighting with the coefficient on the signals regenerated from the symbol. So only part of the interference produced by the symbol of the users is removed during the MAI eliminating process. The method has better performance than that of the traditional PIC method, especially at circumstance of low signal-to-noise ratio, and its improvement in performance is perfectly obvious.
Although both of the two methods above effectively improve performance of traditional PIC method under low signal-to-noise ratio, the extent is very limited.